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The Geospatial Capabilities of Microsoft Fabric and ESRI GeoAnalytics, Demonstrated
- Geospatial data plays a crucial role in data collected and maintained by governments. Big Data engines need adaptation to efficiently handle geospatial data, with considerations like geographical indexes and partitioning.
- Microsoft Fabric Spark compute engine, integrated with ESRI GeoAnalytics, is showcased for geospatial big data processing.
- GeoAnalytics functions in Fabric support over 150 spatial functions, enabling spatial operations in Python, SQL, or Scala with spatial indexing for efficiency.
- A demonstration using Dutch AHN and BAG datasets illustrates spatial selection and processing capabilities on a large dataset.
- Steps include reading data in geoparquet format, spatial selections, aggregation of lidar points, and spatial regression.
- Notable functions like make_point, srid, AggregatePoints, and GWR are used in the demonstration for data transformation and analysis.
- Visualizations are generated to showcase building data and height differences, emphasizing the importance of geographical data in analytics.
- Challenges of handling geospatial data efficiently in big data systems are discussed, emphasizing the need for adaptation and specialized tools.
- The blog post serves as a demonstration of effective geospatial big data processing using Microsoft Fabric and ESRI GeoAnalytics.
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